Sociodemographic variation in use of and preferences for digital technologies among patients in primary care
Bibliographic record
Abstract
OBJECTIVE: To assess the association between patient sociodemographic characteristics and adoption of and preferences for digital technologies in primary care. DESIGN: Cross-sectional bilingual online survey conducted in the fall of 2022. SETTING: Canada. PARTICIPANTS: Adults living in Canada aged 18 and older. MAIN OUTCOME MEASURES: Descriptive statistics were reviewed and a bivariate analysis was conducted of 8 outcomes by sociodemographic characteristic. Models included the following 8 self-reported characteristics: gender, age, province, level of education, level of income, rurality, whether the participant was born in Canada, and health status. Descriptive responses to a question on why video appointments were not important for some respondents were also examined. RESULTS: Data were analyzed from 9279 completed responses. Compared to those earning more than $150,000, respondents earning less than $30,000 were less likely to have recently used email or secure messaging (adjusted odds ratio [aOR]=0.57, 95% CI 0.37 to 0.87) or video calls (aOR=0.65, 95% CI 0.31 to 1.37) or want to use email or secure messaging (aOR=0.71, 95% CI 0.51 to 0.97) or video calls (aOR=0.50, 95% CI 0.36 to 0.68). Compared to university graduates, respondents with a high school diploma or below were less likely to have used email or secure messaging (aOR=0.67, 95% CI 0.49 to 0.90) or video calls (aOR=0.42, 95% CI 0.24 to 0.76) or want to use email or secure messaging (aOR=0.74, 95% CI 0.60 to 0.91) or video calls (aOR=0.73, 95% CI 0.59 to 0.90). People earning less than $30,000 were less likely to have accessed personal health records (aOR=0.43, 95% CI 0.30 to 0.61) or place importance on accessing them (aOR=0.60, 95% CI 0.41 to 0.88). Similarly, people with a high school diploma or less were less likely to access personal health records (aOR=0.61, 95% CI 0.50 to 0.76) and place importance on accessing them (aOR=0.68, 95% CI 0.54 to 0.86). CONCLUSION: The results suggest that people living with a lower income or who have less formal education are less likely to have used digital technologies or consider them important. Further research and policy work should help to understand barriers to adoption of digital technologies and develop tailored interventions to enable equitable access to health care services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".